An ecosystem has emerged around low-cost retail tools as inexpensive and tax-efficient ETFs have enabled a new generation of roboadvisors expanding financial advice beyond the mass affluent and middle class.

The availability of alternative data has allowed hedge fund managers to reinvent themselves as “quantamental”, incorporating new machine learning tools into fundamental human analysis as the industry faces scrutiny over high fees and underperformance.

A second wave of personal savings and investment tools have begun to further democratize access to financial services to previously underserved segments.

The asset management industry has been among the most visibly affected by the development of automation and AI technology. After the global financial crisis, trillions in asset purchases by global central banks have left stockpickers struggling to outperform distorted market benchmarks. This trend has culled from both active mutual fund and fundamental hedge fund managers. The former have faced further disruption with low-fee passive vehicles replacing active funds. This has spurred the development of an ecosystem with roboadvisors using online and mobile tools to help retail clients with automated savings and asset allocation into these low-fee investment vehicles. Furthermore, the most sophisticated investors have also been forced to fight tooth and nail to justify high fees amidst challenging performance. This has led to a technology arms race across the entire industry.

Hedge Funds & Alternative Data

Savvy hedge funds have been better positioned to fight back than their counterparts who serve Main Street. Institutional asset allocators have shown a preference for funds trading on quantitative strategies and algorithms rather than the fundamental strategies of years past. Out of necessity, the savviest funds have begun to reinvent themselves as “quantamental.” The made-up term refers to the marriage of quantitative tools to fundamental analysis. This hybrid strategy typically relies on “alternative data.” These datasets include remote observation from satellites and drones, IoT data logs from smartphones cars and other devices, social media, email receipt scraping and location monitoring such as shipping traffic. The largest funds have the resources to invest in these alternative datasets and tools. Historically, larger funds struggled to outperform their smaller and nimbler peers due to capacity constraints around market liquidity and concentration of positions.

However, this alternative data and AI arms race threatens to level the playing field. The primary tool quant funds use to extract value from alternative data is machine learning. Models learn from the data rather than via pre-programmed rules designed by humans. Supervised and unsupervised machine learning techniques can be time consuming and resource intensive. Recently, a technique called deep learning attempts to replicate the way that humans learn new information via deep neural networks with hidden layers. This technique has begun to prove useful on visual datasets which can be collected via satellite and drone data.

Democratization of Financial Advice

A major theme when speaking to executives and investors in the investment technology space has been the goal of financial inclusion and democratization of financial services. Historically, bespoke counsel on asset allocation and major financial decisions had been available only to the mass affluent. Recently, mobile technology has allowed startup platforms to fund broader access to these services funded by eager VCs. Some of the highestprofile fintech startups have fit into this trend. Current events have highlighted the stark differences and opportunities that exist even within various socioeconomic spheres within the US. Bringing financial advice and access to scalable investment opportunities has been a major theme for fintech startups. This can take the form of account minimums in the single digits, fees as a percentage of assets or $1/month, and no commissions. Unlike Gen Xers, many of whom dabbled in day trading in the 1990s, millennials have shown less of a predilection for stockpicking. Many platforms that do allow for user-directed investments group stocks into baskets based around themes. These often include things like national security, health and wellness, or clean energy.

It makes sense for the largest roboadvisors to pursue this strategy as there is less competition for this segment, and it makes for a good pitch to bleeding-heart VCs. SoFi, the company best positioned in the US to become a fintech conglomerate, has begun offering wealth management services to “members.” The company has served a HENRY (high-earner not rich yet) clientele by offering student loan refinance to graduates of elite universities and, more recently, home mortgages. Mass affluent boomers have long been the sweet spot of traditional advisory services; most startups aim to firmly establish themselves in another niche before expanding to all segments.

Analysis of Private Investment

So far 2017 has seen a deceleration in investment in fintech companies attacking the asset management space. However, much of this trend has been merely the absence of major Chinese players raising multi-billion dollar rounds. The huge spike seen in 2016 was due to Ant Financial raising $4.5 billion primarily from Chinese state-owned enterprises. While US-based roboadvisors Personal Capital and Betterment recently raised rounds in the $100 million range, the smaller round sizes reveal maturing business models and near profitability for the largest US players.

Select Company Profiles

Stash

Location: New York, NY

Year Founded: 2015 | Capital Raised to Date: $79.25M

First Funding Date: February 2016 | First Funding Amount: $3M

Latest Funding Date: July 2017 | Latest Funding Amount: $42M

Latest Funding Post-Valuation: $264M

Description: In spite of only existing for less than two years, the company has already reached a post-valuation of $264 million when it recently raised $42 million from Coatue, Breyer Capital, Goodwater Capital and Valar Ventures. It took Betterment five years to reach the same valuation. The company has pursued a strategy of reaching underserved markets for savings and investment with a $5 minimum and fees of $1/month. The company’s most recent ADV filing reveals $62 million in AUM and 540,000 client accounts, revealing an average account size around $100. The company’s fee structure of $1/month up to $5,000 makes the math check out. Even so, the company has barely tapped into their total addressable market and will use VC funding to scale operations.

Betterment

Location: New York, NY

Year Founded: 2007 | Capital Raised to Date: $275M

First Funding Date: November 2010 | First Funding Amount: $3M

Latest Funding Date: July 2017 | Latest Funding Amount: $70M |

Latest Funding Post-Valuation: $800M

Description: The company recently raised $70 million at an $800 million post-valuation. The company announced alongside the fundraising that it will use the capital injection to scale tools it had only previously made available to investors with more than $100,000 on the platform. These tools include free access to financial planning professionals. The firm manages just shy of $10 billion in client assets.